```json
{
    "detail": [
        {
            "type": "extra_forbidden",
            "loc": ["body", "extra"],
            "msg": "Extra inputs are not permitted",
            "input": "Mr. Poopybutthole"
        }
    ]
}
```

## Resumo

0;\n}\n\n.input-group > .input-group-append > .btn,\n.input-group > .input-group-append > .input-group-text,\n.input-group > .input-group-prepend:not(:first-child) > .btn,\n.input-group > .input-group-prepend:not(:first-child) > .input-group-text,\n.input-group > .input-group-prepend:first-child > .btn:not(:first-child),\n.input-group > .input-group-prepend:first-child > .input-group-text:not(:first-child) {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n\n.custom-control {\n  position:...

				"us-west-1",
				"s3",
				"ABCD"),
			expectedCredentials: credentialHeader{},
			expectedErrCode:     ErrInvalidRequestVersion,
		},
		// Test Case - 9.
		// Test case with right inputs. Expected to return a valid CredentialHeader.
		// "aws4_request" is the valid request version.
		{
			inputCredentialStr: generateCredentialStr(
				"Z7IXGOO6BZ0REAN1Q26I",
				sampleTimeStr,
				"us-west-1",

Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate

```json
{
    "detail": [
        {
            "type": "extra_forbidden",
            "loc": ["header", "tool"],
            "msg": "Extra inputs are not permitted",
            "input": "plumbus",
        }
    ]
}
```

## Summary

```json
{
    "detail": [
        {
            "type": "extra_forbidden",
            "loc": ["query", "tool"],
            "msg": "Extra inputs are not permitted",
            "input": "plumbus"
        }
    ]
}
```

## Summary

You can use **Pydantic models** to declare **query parameters** in **FastAPI**. 😎

/// tip

        javadocs = objects.newInstance(Javadocs.class);
        kotlinDslReference = objects.newInstance(KotlinDslReference.class);
    }

    /**
     * The root directory of all documentation inputs
     */
    public abstract DirectoryProperty getSourceRoot();

    /**
     * Collection of CSS files to include in generated documentation.
     */
    public abstract ConfigurableFileCollection getCssFiles();

```json
{
    "detail": [
        {
            "type": "extra_forbidden",
            "loc": ["cookie", "santa_tracker"],
            "msg": "Extra inputs are not permitted",
            "input": "good-list-please",
        }
    ]
}
```

## Summary

```json
{
    "detail": [
        {
            "type": "extra_forbidden",
            "loc": ["header", "tool"],
            "msg": "Extra inputs are not permitted",
            "input": "plumbus",
        }
    ]
}
```

## Resumo

    return underlying_devices_;
  }

  // Takes a description of a single operation being executed on the
  // ParallelDevice, and in turn runs one operation per component device with
  // its corresponding inputs from the input ParallelTensors. Wraps the
  // resulting per-device and per-output TFE_TensorHandles into one
  // ParallelTensor per output of the original operation.
  //
  // Attributes are forwarded to executed operations unmodified.